Carbon based materials are really an integral component of our lives and widespread research regarding their properties was conducted along this process. The addition of dopants to carbon materials, either during the production process or later on, has been actively investigated by researchers all over the world who are looking into how doping can enhance the performance of materials and how to overcome the current difficulties. This study explores synthesis methods for nitrogen-doped carbon materials, focusing on advancements in adsorption of different pollutants like CO₂ from air and organic, inorganic and ions pollutants from water, energy conversion, and storage, offering novel solutions to environmental and energy challenges. It addresses current issues with nitrogen-doped carbon materials, aiming to contribute to sustainable solutions in environmental and energy sciences. Alongside precursor types and synthesis methods, a significant relationship exists between nitrogen content percentage and adsorption capacity in nitrogen-doped activated carbon. Nitrogen content ranges from 0.64% to 11.23%, correlating with adsorption capacities from 0.05 mmol/g to 7.9 mmol/g. Moreover, an electrochemical correlation is observed between nitrogen atom increase and specific capacity in nitrogen-doped activated carbon electrodes. Higher nitrogen percentage corresponds to increased specific capacity and capacity retention. This comprehensive analysis sheds light on the potential of nitrogen-doped carbon materials and highlights their significance in addressing critical environmental and energy challenges.

The study explores improving opportunities of forecasting accuracy from the traditional method through advanced forecasting techniques. This enables companies to optimize inventory management, production planning, and reducing the travelling time thorough vehicle route optimization. The article introduced a holistic framework by deploying advanced demand forecasting techniques i.e., AutoRegressive Integrated Moving Average (ARIMA) and Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) models, and the Vehicle Routing Problem with Time Windows (VRPTW) approach. The actual milk demand data came from the company and two forecasting models, ARIMA and RNN-LSTM, have been deployed using Python Jupyter notebook and compared them in terms of various precision measures. VRPTW established not only the optimal routes for a fleet of six vehicles but also tactical scheduling which contributes to a streamlined and agile raw milk collection process, ensuring a harmonious and resource-efficient operation. The proposed approach succeeded on dropping about 16% of total travel time and capable of making predictions with approximately 2% increased accuracy than before.

This study aims to identify the causes of delays in public construction projects in Thailand, a developing country. Increasing construction durations lead to higher costs, making it essential to pinpoint the causes of these delays. The research analyzed 30 public construction projects that encountered delays. Delay causes were categorized into four groups: contractor-related, client-related, supervisor-related, and external factors. A questionnaire was used to survey these causes, and the Relative Importance Index (RII) method was employed to prioritize them. The findings revealed that the primary cause of delays was contractor-related financial issues, such as cash flow problems, with an RII of 0.777 and a weighted value of 84.44%. The second most significant cause was labor issues, such as a shortage of workers during the harvest season or festivals, with an RII of 0.773. Additionally, various algorithms were used to compare the Relative Importance Index (RII) and four machine learning methods: Decision Tree (DT), Deep Learning, Neural Network, and Naïve Bayes. The Deep Learning model proved to be the most effective baseline model, achieving a 90.79% accuracy rate in identifying contractor-related financial issues as a cause of construction delays. This was followed by the Neural Network model, which had an accuracy rate of 90.26%. The Decision Tree model had an accuracy rate of 85.26%. The RII values ranged from 68.68% for the Naïve Bayes model to 77.70% for the highest RII model. The research results indicate that contractor financial liquidity and costs significantly impact construction operations, which public agencies must consider. Additionally, the availability of contractor labor is crucial for the continuity of projects. The accuracy and reliability of the data obtained using advanced data mining techniques demonstrate the effectiveness of these results. This can be efficiently utilized by stakeholders involved in construction projects in Thailand to enhance construction project management.

The selection of a suitable place for an activity is an important decision made for a project, which requires assessing it from different points of view. Educational use is one of the most complicated and substantial uses in urban space that requires precise and logical attention to its location and neighborhood with similar and consistent uses. Faculties of universities are educational spaces that should be protected against physical and moral damage to create a healthy educational environment. To do this, it is necessary to find and assess the factors affecting the location of educational spaces. The extant study aimed at finding and assessing the factors affecting the location of educational spaces to locate art and architecture schools or faculties in 4 important universities. The present study is applied developmental research in terms of nature and descriptive-analytical in terms of method. This study used the AHP (Analytical Hierarchy Process) weighing and controlled the prioritization through the TOPSIS (Technique for Order Preference by Similarity) technique in the methodology phase. Since there was no criterion and metric for these centers, six were chosen as the primary metrics after reviewing the relevant theoretical foundations, early investigations, and collecting effective data. Finally, the results indicated the most important factors of vehicular or roadway access, pedestrian access, slope, parking, adjacency, neighborhood, and area. Among the mentioned factors, pedestrian access (w: 0.4231) had the highest weight and was the priority in the location of architecture faculty in studied campuses and areas inside the universities.

A gradually detailed geophysical investigation took place on Ancient Marina territory. In that area was extended Ancient Tritaea, according to responsible Archaeological Services. The first approach had been attempted since 1988 by applied electric mapping based on a twin-probe array. Later, the survey extended to the peripheral zone under the relative request from the 6th Archaeological Antiquity. A new approach was implemented by combining three different geophysical techniques, like electrical mapping, total intensity, and vertical gradient. These were applied on discrete geophysical grids. Electric mapping tried to separate the area into low and high-interest subareas according to soil resistance allocation. That technique detected enough geometrical characteristics, which worked as the main lever for the application of two other geophysical techniques. The other two techniques would be to certify the existence of geometrical characteristics, which divorced them from geological findings. Magnetic methods were characterized as a rapid technique with greater sensitivity in relation to electric mapping. Also, vertical gradient focuses on the horizontal extension of buried remains. Processing of magnetic measurements (total and vertical) certified the results from electric mapping. Also, both of the techniques confirmed the existence of human activity results, which were presented as a cross-section of two perpendicular parts. The new survey results showed that the new findings related to results from the previous approach. Geophysical research in that area is continuing.

In this paper advanced Sentiment Analysis techniques were applied to evaluate public opinions reported by rail users with respect to four major European railway companies, i.e., Trenitalia and Italo in Italy, SNCF in France and Renfe in Spain. Two powerful language models were used, RoBERTa and BERT, to analyze big amount of text data collected from a social platform dedicated to customers reviews, i.e., TrustPilot. Data concerning the four European railway companies were first collected and classified into subcategories related to different aspects of the railway sector, such as train punctuality, quality of on-board services, safety, etc. Then, the RoBERTa and BERT models were developed to understand context and nuances of natural language. This study provides a useful support for railways companies to promote strategies for improving their service.